Overview

Dataset statistics

Number of variables11
Number of observations4319
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory371.3 KiB
Average record size in memory88.0 B

Variable types

Numeric11

Alerts

df_index is highly correlated with purchases_no and 1 other fieldsHigh correlation
gross_revenue is highly correlated with purchases_no and 3 other fieldsHigh correlation
recency_days is highly correlated with purchases_no and 1 other fieldsHigh correlation
purchases_no is highly correlated with df_index and 6 other fieldsHigh correlation
products_no is highly correlated with gross_revenue and 3 other fieldsHigh correlation
items_no is highly correlated with gross_revenue and 3 other fieldsHigh correlation
frequency is highly correlated with purchases_no and 1 other fieldsHigh correlation
returns_no is highly correlated with satisfaction_rateHigh correlation
satisfaction_rate is highly correlated with returns_noHigh correlation
recorrence is highly correlated with df_index and 6 other fieldsHigh correlation
df_index is highly correlated with recorrenceHigh correlation
gross_revenue is highly correlated with purchases_no and 1 other fieldsHigh correlation
purchases_no is highly correlated with gross_revenue and 3 other fieldsHigh correlation
products_no is highly correlated with purchases_noHigh correlation
items_no is highly correlated with gross_revenue and 1 other fieldsHigh correlation
frequency is highly correlated with recorrenceHigh correlation
recorrence is highly correlated with df_index and 2 other fieldsHigh correlation
gross_revenue is highly correlated with purchases_no and 3 other fieldsHigh correlation
purchases_no is highly correlated with gross_revenue and 3 other fieldsHigh correlation
products_no is highly correlated with gross_revenue and 3 other fieldsHigh correlation
items_no is highly correlated with gross_revenue and 3 other fieldsHigh correlation
returns_no is highly correlated with satisfaction_rateHigh correlation
satisfaction_rate is highly correlated with returns_noHigh correlation
recorrence is highly correlated with gross_revenue and 3 other fieldsHigh correlation
df_index is highly correlated with recency_days and 1 other fieldsHigh correlation
gross_revenue is highly correlated with purchases_no and 3 other fieldsHigh correlation
recency_days is highly correlated with df_indexHigh correlation
purchases_no is highly correlated with gross_revenue and 4 other fieldsHigh correlation
products_no is highly correlated with gross_revenue and 3 other fieldsHigh correlation
items_no is highly correlated with gross_revenue and 3 other fieldsHigh correlation
returns_no is highly correlated with gross_revenue and 3 other fieldsHigh correlation
recorrence is highly correlated with df_index and 1 other fieldsHigh correlation
gross_revenue is highly skewed (γ1 = 20.83762299) Skewed
items_no is highly skewed (γ1 = 22.20471768) Skewed
returns_no is highly skewed (γ1 = 27.00404142) Skewed
df_index is uniformly distributed Uniform
df_index has unique values Unique
customer_id has unique values Unique
returns_no has 2829 (65.5%) zeros Zeros

Reproduction

Analysis started2022-09-08 15:01:51.043289
Analysis finished2022-09-08 15:02:23.181953
Duration32.14 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct4319
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2179.25793
Minimum0
Maximum4345
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size33.9 KiB
2022-09-08T12:02:23.390725image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile220.9
Q11098.5
median2183
Q33262.5
95-th percentile4129.1
Maximum4345
Range4345
Interquartile range (IQR)2164

Descriptive statistics

Standard deviation1253.017453
Coefficient of variation (CV)0.5749743691
Kurtosis-1.194978742
Mean2179.25793
Median Absolute Deviation (MAD)1082
Skewness-0.007301569894
Sum9412215
Variance1570052.738
MonotonicityStrictly increasing
2022-09-08T12:02:23.700664image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
29091
 
< 0.1%
28951
 
< 0.1%
28961
 
< 0.1%
28971
 
< 0.1%
28981
 
< 0.1%
28991
 
< 0.1%
29001
 
< 0.1%
29011
 
< 0.1%
29021
 
< 0.1%
Other values (4309)4309
99.8%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
43451
< 0.1%
43441
< 0.1%
43431
< 0.1%
43421
< 0.1%
43411
< 0.1%
43401
< 0.1%
43391
< 0.1%
43381
< 0.1%
43371
< 0.1%
43361
< 0.1%

customer_id
Real number (ℝ≥0)

UNIQUE

Distinct4319
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15300.5059
Minimum12347
Maximum18287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.9 KiB
2022-09-08T12:02:23.975599image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum12347
5-th percentile12617.9
Q113815.5
median15299
Q316778.5
95-th percentile17980.4
Maximum18287
Range5940
Interquartile range (IQR)2963

Descriptive statistics

Standard deviation1720.328769
Coefficient of variation (CV)0.1124360711
Kurtosis-1.194800323
Mean15300.5059
Median Absolute Deviation (MAD)1482
Skewness0.001466012037
Sum66082885
Variance2959531.075
MonotonicityNot monotonic
2022-09-08T12:02:24.205703image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
178501
 
< 0.1%
172991
 
< 0.1%
150141
 
< 0.1%
147651
 
< 0.1%
168691
 
< 0.1%
159091
 
< 0.1%
136181
 
< 0.1%
160501
 
< 0.1%
178791
 
< 0.1%
175621
 
< 0.1%
Other values (4309)4309
99.8%
ValueCountFrequency (%)
123471
< 0.1%
123481
< 0.1%
123491
< 0.1%
123501
< 0.1%
123521
< 0.1%
123531
< 0.1%
123541
< 0.1%
123551
< 0.1%
123561
< 0.1%
123571
< 0.1%
ValueCountFrequency (%)
182871
< 0.1%
182831
< 0.1%
182821
< 0.1%
182811
< 0.1%
182801
< 0.1%
182781
< 0.1%
182771
< 0.1%
182761
< 0.1%
182731
< 0.1%
182721
< 0.1%

gross_revenue
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct4239
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1971.368585
Minimum3.75
Maximum279138.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.9 KiB
2022-09-08T12:02:24.502727image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum3.75
5-th percentile112.313
Q1306.635
median668.36
Q31632.775
95-th percentile5726.869
Maximum279138.02
Range279134.27
Interquartile range (IQR)1326.14

Descriptive statistics

Standard deviation8496.008101
Coefficient of variation (CV)4.309700461
Kurtosis560.8900505
Mean1971.368585
Median Absolute Deviation (MAD)463.26
Skewness20.83762299
Sum8514340.92
Variance72182153.66
MonotonicityNot monotonic
2022-09-08T12:02:24.818254image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
76.324
 
0.1%
35.43
 
0.1%
113.53
 
0.1%
4403
 
0.1%
363.653
 
0.1%
110.382
 
< 0.1%
324.242
 
< 0.1%
248.612
 
< 0.1%
251.212
 
< 0.1%
112.752
 
< 0.1%
Other values (4229)4293
99.4%
ValueCountFrequency (%)
3.751
< 0.1%
5.91
< 0.1%
12.751
< 0.1%
152
< 0.1%
171
< 0.1%
20.82
< 0.1%
25.51
< 0.1%
301
< 0.1%
30.61
< 0.1%
32.651
< 0.1%
ValueCountFrequency (%)
279138.021
< 0.1%
259657.31
< 0.1%
194550.791
< 0.1%
140450.721
< 0.1%
124564.531
< 0.1%
117379.631
< 0.1%
91062.381
< 0.1%
72882.091
< 0.1%
66653.561
< 0.1%
65039.621
< 0.1%

recency_days
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct304
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean92.0349618
Minimum0
Maximum373
Zeros34
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size33.9 KiB
2022-09-08T12:02:25.123610image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q117
median50
Q3142
95-th percentile311
Maximum373
Range373
Interquartile range (IQR)125

Descriptive statistics

Standard deviation100.0708639
Coefficient of variation (CV)1.087313581
Kurtosis0.4317306489
Mean92.0349618
Median Absolute Deviation (MAD)40
Skewness1.246543803
Sum397499
Variance10014.1778
MonotonicityNot monotonic
2022-09-08T12:02:25.702553image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1103
 
2.4%
494
 
2.2%
394
 
2.2%
290
 
2.1%
879
 
1.8%
1077
 
1.8%
1774
 
1.7%
771
 
1.6%
970
 
1.6%
2264
 
1.5%
Other values (294)3503
81.1%
ValueCountFrequency (%)
034
 
0.8%
1103
2.4%
290
2.1%
394
2.2%
494
2.2%
548
1.1%
771
1.6%
879
1.8%
970
1.6%
1077
1.8%
ValueCountFrequency (%)
37317
0.4%
37217
0.4%
3716
 
0.1%
3693
 
0.1%
3685
 
0.1%
3675
 
0.1%
36610
0.2%
36510
0.2%
3646
 
0.1%
3626
 
0.1%

purchases_no
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct56
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.259550822
Minimum1
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.9 KiB
2022-09-08T12:02:25.978986image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q35
95-th percentile13
Maximum206
Range205
Interquartile range (IQR)4

Descriptive statistics

Standard deviation7.657865258
Coefficient of variation (CV)1.797810515
Kurtosis244.1834183
Mean4.259550822
Median Absolute Deviation (MAD)1
Skewness11.95442164
Sum18397
Variance58.64290031
MonotonicityNot monotonic
2022-09-08T12:02:26.329596image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11492
34.5%
2827
19.1%
3504
 
11.7%
4394
 
9.1%
5237
 
5.5%
6173
 
4.0%
7138
 
3.2%
898
 
2.3%
969
 
1.6%
1055
 
1.3%
Other values (46)332
 
7.7%
ValueCountFrequency (%)
11492
34.5%
2827
19.1%
3504
 
11.7%
4394
 
9.1%
5237
 
5.5%
6173
 
4.0%
7138
 
3.2%
898
 
2.3%
969
 
1.6%
1055
 
1.3%
ValueCountFrequency (%)
2061
< 0.1%
1991
< 0.1%
1241
< 0.1%
971
< 0.1%
912
< 0.1%
861
< 0.1%
721
< 0.1%
622
< 0.1%
601
< 0.1%
571
< 0.1%

products_no
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct468
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean91.74878444
Minimum1
Maximum7838
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.9 KiB
2022-09-08T12:02:26.648840image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q117
median41
Q3100
95-th percentile315.1
Maximum7838
Range7837
Interquartile range (IQR)83

Descriptive statistics

Standard deviation228.8353351
Coefficient of variation (CV)2.494151137
Kurtosis482.3917998
Mean91.74878444
Median Absolute Deviation (MAD)30
Skewness18.08994157
Sum396263
Variance52365.61057
MonotonicityNot monotonic
2022-09-08T12:02:26.984512image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1085
 
2.0%
677
 
1.8%
975
 
1.7%
170
 
1.6%
1569
 
1.6%
1168
 
1.6%
867
 
1.6%
566
 
1.5%
2865
 
1.5%
765
 
1.5%
Other values (458)3612
83.6%
ValueCountFrequency (%)
170
1.6%
250
1.2%
355
1.3%
448
1.1%
566
1.5%
677
1.8%
765
1.5%
867
1.6%
975
1.7%
1085
2.0%
ValueCountFrequency (%)
78381
< 0.1%
56731
< 0.1%
50951
< 0.1%
45801
< 0.1%
26981
< 0.1%
23791
< 0.1%
20601
< 0.1%
18181
< 0.1%
16731
< 0.1%
16371
< 0.1%

items_no
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct1760
Distinct (%)40.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1157.266728
Minimum1
Maximum196844
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.9 KiB
2022-09-08T12:02:27.328115image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile47
Q1160.5
median379
Q3991.5
95-th percentile3542.1
Maximum196844
Range196843
Interquartile range (IQR)831

Descriptive statistics

Standard deviation4775.918514
Coefficient of variation (CV)4.126895206
Kurtosis731.6935971
Mean1157.266728
Median Absolute Deviation (MAD)276
Skewness22.20471768
Sum4998235
Variance22809397.65
MonotonicityNot monotonic
2022-09-08T12:02:27.677504image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8818
 
0.4%
12017
 
0.4%
8416
 
0.4%
14615
 
0.3%
12815
 
0.3%
14415
 
0.3%
7215
 
0.3%
15014
 
0.3%
10614
 
0.3%
20013
 
0.3%
Other values (1750)4167
96.5%
ValueCountFrequency (%)
11
 
< 0.1%
25
0.1%
33
0.1%
47
0.2%
53
0.1%
63
0.1%
71
 
< 0.1%
81
 
< 0.1%
92
 
< 0.1%
105
0.1%
ValueCountFrequency (%)
1968441
< 0.1%
802631
< 0.1%
773731
< 0.1%
699931
< 0.1%
645491
< 0.1%
641241
< 0.1%
633121
< 0.1%
583431
< 0.1%
578851
< 0.1%
502551
< 0.1%

frequency
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct1226
Distinct (%)28.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4028513354
Minimum0.005449591281
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.9 KiB
2022-09-08T12:02:27.987786image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.005449591281
5-th percentile0.0101010101
Q10.0200904119
median0.04545454545
Q31
95-th percentile1
Maximum17
Range16.99455041
Interquartile range (IQR)0.9799095881

Descriptive statistics

Standard deviation0.5599672117
Coefficient of variation (CV)1.390009571
Kurtosis178.2198375
Mean0.4028513354
Median Absolute Deviation (MAD)0.03354978355
Skewness6.706318483
Sum1739.914917
Variance0.3135632782
MonotonicityNot monotonic
2022-09-08T12:02:28.295160image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11500
34.7%
248
 
1.1%
0.0277777777817
 
0.4%
0.062517
 
0.4%
0.0238095238116
 
0.4%
0.0833333333315
 
0.3%
0.0344827586215
 
0.3%
0.0909090909115
 
0.3%
0.0294117647114
 
0.3%
0.0357142857113
 
0.3%
Other values (1216)2649
61.3%
ValueCountFrequency (%)
0.0054495912811
 
< 0.1%
0.0054644808741
 
< 0.1%
0.0054794520551
 
< 0.1%
0.0054945054951
 
< 0.1%
0.0055865921792
< 0.1%
0.0056022408961
 
< 0.1%
0.0056179775282
< 0.1%
0.005665722381
 
< 0.1%
0.0056818181822
< 0.1%
0.0056980056983
0.1%
ValueCountFrequency (%)
171
 
< 0.1%
41
 
< 0.1%
35
 
0.1%
248
 
1.1%
1.1428571431
 
< 0.1%
11500
34.7%
0.751
 
< 0.1%
0.66666666673
 
0.1%
0.5508021391
 
< 0.1%
0.53351206431
 
< 0.1%

returns_no
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct205
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.94327391
Minimum0
Maximum9014
Zeros2829
Zeros (%)65.5%
Negative0
Negative (%)0.0%
Memory size33.9 KiB
2022-09-08T12:02:28.580352image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile57
Maximum9014
Range9014
Interquartile range (IQR)3

Descriptive statistics

Standard deviation233.2881314
Coefficient of variation (CV)10.1680402
Kurtosis891.6164328
Mean22.94327391
Median Absolute Deviation (MAD)0
Skewness27.00404142
Sum99092
Variance54423.35227
MonotonicityNot monotonic
2022-09-08T12:02:28.909195image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02829
65.5%
1169
 
3.9%
2149
 
3.4%
3105
 
2.4%
489
 
2.1%
678
 
1.8%
561
 
1.4%
1251
 
1.2%
744
 
1.0%
843
 
1.0%
Other values (195)701
 
16.2%
ValueCountFrequency (%)
02829
65.5%
1169
 
3.9%
2149
 
3.4%
3105
 
2.4%
489
 
2.1%
561
 
1.4%
678
 
1.8%
744
 
1.0%
843
 
1.0%
941
 
0.9%
ValueCountFrequency (%)
90141
< 0.1%
80041
< 0.1%
44271
< 0.1%
37681
< 0.1%
33321
< 0.1%
28781
< 0.1%
20221
< 0.1%
20121
< 0.1%
17761
< 0.1%
15941
< 0.1%

satisfaction_rate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct1378
Distinct (%)31.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9865749884
Minimum0.01369863014
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.9 KiB
2022-09-08T12:02:29.237360image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.01369863014
5-th percentile0.9422317612
Q10.995456996
median1
Q31
95-th percentile1
Maximum1
Range0.9863013699
Interquartile range (IQR)0.004543004033

Descriptive statistics

Standard deviation0.05521964059
Coefficient of variation (CV)0.05597105263
Kurtosis79.17949446
Mean0.9865749884
Median Absolute Deviation (MAD)0
Skewness-7.902730168
Sum4261.017375
Variance0.003049208707
MonotonicityNot monotonic
2022-09-08T12:02:29.540920image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12829
65.5%
0.98924731184
 
0.1%
0.99033816434
 
0.1%
0.99074074073
 
0.1%
0.9767441863
 
0.1%
0.98507462693
 
0.1%
0.97619047623
 
0.1%
0.9878048783
 
0.1%
0.97560975613
 
0.1%
0.99769053123
 
0.1%
Other values (1368)1461
33.8%
ValueCountFrequency (%)
0.013698630141
< 0.1%
0.16666666671
< 0.1%
0.36666666671
< 0.1%
0.38848920861
< 0.1%
0.39911634761
< 0.1%
0.40354330711
< 0.1%
0.43511450381
< 0.1%
0.43536121671
< 0.1%
0.43979591841
< 0.1%
0.46009389671
< 0.1%
ValueCountFrequency (%)
12829
65.5%
0.99988303641
 
< 0.1%
0.99981600741
 
< 0.1%
0.99971830991
 
< 0.1%
0.99968592961
 
< 0.1%
0.99963807461
 
< 0.1%
0.99963675991
 
< 0.1%
0.99963623141
 
< 0.1%
0.99963289281
 
< 0.1%
0.99960691821
 
< 0.1%

recorrence
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.957860616
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.9 KiB
2022-09-08T12:02:29.824306image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile9
Maximum12
Range11
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.516289249
Coefficient of variation (CV)0.8507125846
Kurtosis2.506514334
Mean2.957860616
Median Absolute Deviation (MAD)1
Skewness1.685040046
Sum12775
Variance6.331711587
MonotonicityNot monotonic
2022-09-08T12:02:30.081440image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
11630
37.7%
2919
21.3%
3527
 
12.2%
4388
 
9.0%
5265
 
6.1%
6165
 
3.8%
7101
 
2.3%
891
 
2.1%
971
 
1.6%
1258
 
1.3%
Other values (2)104
 
2.4%
ValueCountFrequency (%)
11630
37.7%
2919
21.3%
3527
 
12.2%
4388
 
9.0%
5265
 
6.1%
6165
 
3.8%
7101
 
2.3%
891
 
2.1%
971
 
1.6%
1055
 
1.3%
ValueCountFrequency (%)
1258
 
1.3%
1149
 
1.1%
1055
 
1.3%
971
 
1.6%
891
 
2.1%
7101
 
2.3%
6165
 
3.8%
5265
6.1%
4388
9.0%
3527
12.2%

Interactions

2022-09-08T12:02:19.882130image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:01:53.940614image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:01:56.201710image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:01:58.774386image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:01.182770image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:03.836541image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:06.324373image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:09.096628image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:12.143150image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:14.760958image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:17.321916image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:20.094830image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:01:54.152454image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:01:56.373532image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:01:58.954190image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:01.423340image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:04.059849image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:06.572554image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:09.346825image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:12.361696image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:14.976528image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:17.548825image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:20.306324image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:01:54.364599image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:01:56.583364image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:01:59.173606image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:01.661596image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:04.292058image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:06.824209image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:09.606676image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:12.608499image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:15.211339image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:17.749179image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:20.548283image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:01:54.537239image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:01:56.796351image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:01:59.388238image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:01.867982image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:04.491232image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:07.077922image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:09.857416image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:12.832263image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:15.446552image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:17.988887image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:20.757594image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:01:54.739388image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:01:57.005824image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:01:59.619163image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:02.121558image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:04.732341image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:07.333031image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:10.116090image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:13.077415image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:15.677320image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:18.242943image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:20.980732image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:01:54.954828image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:01:57.186520image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:01:59.824436image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:02.339686image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:04.945579image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:07.580414image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:10.372177image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:13.279308image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:15.902509image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:18.464124image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:21.202790image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:01:55.136826image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:01:57.413775image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:00.050959image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:02.597941image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:05.169204image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:07.812369image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:10.644178image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:13.537849image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:16.154549image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:18.726408image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:21.457087image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:01:55.348885image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:01:57.638559image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:00.288328image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:02.845173image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:05.422462image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:08.078872image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:11.165795image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:13.799810image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:16.403144image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:18.968431image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:21.676543image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:01:55.571296image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:01:57.853903image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:00.500241image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:03.107845image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:05.668233image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:08.348399image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:11.440158image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:14.058276image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:16.648910image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:19.188888image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:21.898704image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:01:55.774252image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:01:58.054566image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:00.709451image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:03.345896image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:05.860712image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:08.591636image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:11.653572image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:14.263754image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:16.880330image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:19.397205image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:22.128437image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:01:55.991095image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:01:58.278638image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:00.950655image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:03.595735image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:06.088483image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:08.848193image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:11.901202image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:14.509807image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:17.093436image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-08T12:02:19.642485image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-09-08T12:02:30.309543image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-09-08T12:02:30.639069image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-09-08T12:02:30.959080image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-09-08T12:02:31.286959image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-09-08T12:02:22.505328image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-09-08T12:02:23.026280image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexcustomer_idgross_revenuerecency_dayspurchases_noproducts_noitems_nofrequencyreturns_nosatisfaction_raterecorrence
00178505391.21372.034.0297.01733.017.00000040.00.9769191.0
11130473232.5956.09.0171.01390.00.02830235.00.9748207.0
22125836705.382.015.0232.05028.00.04032350.00.99005611.0
3313748948.2595.05.028.0439.00.0179210.01.0000003.0
4415100876.00333.03.03.080.00.07317122.00.7250002.0
55152914623.3025.014.0102.02102.00.04011529.00.9862047.0
66146885630.877.021.0327.03621.00.057221399.00.88980911.0
77178095411.9116.012.061.02057.00.03352041.00.9800688.0
881531160767.900.091.02379.038194.00.243316474.00.98759012.0
99160982005.6387.07.067.0613.00.0243900.01.0000007.0

Last rows

df_indexcustomer_idgross_revenuerecency_dayspurchases_noproducts_noitems_nofrequencyreturns_nosatisfaction_raterecorrence
430943361600012393.702.03.09.05110.03.00.01.0000001.0
43104337151953861.002.01.01.01404.01.00.01.0000001.0
4311433814087194.422.01.069.0251.01.01.00.9960161.0
4312433914204161.032.01.044.082.01.00.01.0000001.0
4313434015471469.482.01.077.0266.01.00.01.0000001.0
4314434113436196.891.01.012.076.01.00.01.0000001.0
4315434215520343.501.01.018.0314.01.00.01.0000001.0
4316434313298360.001.01.02.096.01.00.01.0000001.0
4317434414569227.391.01.012.079.01.00.01.0000001.0
4318434512713794.550.01.037.0505.01.00.01.0000001.0